This is the first year we’ve had access to the new ‘Release Spin Rate’ stat, which can be found hiding in a little nook of Baseball Savant. This spin rate, as I understand it, is measured using Doppler Radar at the moment the ball leaves the pitcher’s hand. I’m not exactly sure how the system defines this release point, perhaps when the ball begins to slow down. No matter the case, this new ‘Release Spin Rate’ stat appears to have some potential as a new way of evaluating pitcher performance, since we all assume there must be some correlation between spin rate and success rate as a pitcher.

Lets get some Physics out of the way.

I want to preface this by saying this isn’t meant to be a physics lesson, I’m intentionally oversimplifying everything. I just need to cover some basics before I can move on. I’ll post some links at the bottom if you’re interested in more information.

When a ball is spinning, half of the ball is moving in one direction, and the other is moving in the opposite direction. For example, as the earth rotates, half of it goes into sunlight and the other half goes into darkness. If you move this spinning ball, one part of the ball will be both rotating and moving in the same direction, while the other side of the ball is rotating opposite to the direction of movement. The part of the ball that rotates towards the direction of its movement fights against the air that is trying to brush past it, and it builds a little high pressure region in the air as it moves. You can think of it as the ball pushing against the air, and as you know from the third law of motion, every force has an equal and opposite force. When the ball pushes against the air, the air pushes against the ball. It pushes from this high pressure region, pushing the ball away from the high pressure. This force is called the Magnus Force. Read the rest of this entry »

Back when Defense Independent Pitching stats were originally developed, BABIP was treated almost like a trash bin “other” category for plate appearances that didn’t end with one of the so called “Three True Outcomes.” Strike outs, Walks, and Home runs were observed to have far greater predictive power than other stats with regards to runs scored, and pitcher performance could be accurately estimated using only these three metrics, regardless of other factors. That’s the theory anyway, and it has evolved somewhat to account for new research that has popped up over the years. However, with the Statcast data now publicly available, we now have an unprecedented granular view of batted ball data, and many people, including myself, have developed various methods to apply exit velocity and launch angles to predict both offensive and pitching performance.

I’ve previously written about xOBA, xBABIP, scFIP, VH and PH, each of which aim to estimate end of season results given the (publicly available) Statcast data on each given batted ball. Note, this is all done on a batted ball by batted ball basis, then summed up at the end for each pitcher, team, or what have you. I’m not using average launch angles or average exit velocities to calculate these things.  In calculating and applying these stats I’ve noticed that while xOBA has pretty decent year to year predictive value (2 = .22) and excellent predictive value within a given year (r = .78), xBABIP does not.  Nor does standard BABIP, even though xBABIP is pretty good at predicting BABIP itself.  The two stats, xBABIP and xOBA, are calculated using nearly identical methods, adding just one single step at the end of xOBA, weighting the batted balls by the linear weights you can find here.  How could this be?   Read the rest of this entry »

The calendar has flipped to September, which means we’re down to the final stretch of the season. As a result, there are a plethora of characters and stories circulating through baseball, but at the moment I’m particularly interested in three outfielders; Bryce Harper, Jason Heyward, and Jay Bruce.  Three national league right fielders, each playing for a contender, and each going through various stages and degrees of struggle.

Jason Heyward came into the season with a newly minted 184 million dollar contract, which he has presumably framed somewhere in his house.  If it were me, I’d probably have a copy framed in every room of my house, but that’s neither here nor there.  Bryce Harper entered the season as the reigning NL MVP, with many claiming he was going to officially dethrone Mike Trout as the greatest player in the sport.  Jay Bruce, well, he might not be as accomplished or wealthy as the other two, but was acquired by a New York Mets team that placed huge hopes that not only would he help them down the stretch this season, but also act as leverage to perhaps extend Yoenis Cespedes, or even replace Cespedes altogether next season. Those are big shoes to fill.

Read the rest of this entry »

Last week I wrote about two stats, Value Hit Percent (VH%) and Poorly Hit Percent (PH%), which both serve to measure the quality of contact on the high and low ends of the spectrum, respectively. Value Hits represent the highest quality of contact, making up a huge number of doubles, triples, and home runs, while Poorly Hit balls represent balls in play that are almost automatic outs. Poorly hit balls register in an earned base, whether by a hit or through an error, about 2.5% of the time. This is a success rate roughly on par with infield fly balls, which results in a base about 2.2% of the time, according to the MLB classification of pop ups over the same two year time span. Granted, differing methods for defining pop ups can decrease the success rates dramatically, down to 1% or lower. No matter how you cut it, though, we’re talking about near automatic outs with both PH and IFFB.

Many have combined IFFB rates with strike out rates when calculating FIP, and the calculations work because both the strike out and the infield fly have roughly equivalent run values during a game, sitting between -.25 and -.28 depending on the season. Considering PH have very similar success rates to IFFB, it seems safe to assume their run value is very similar as well, and PH could be added to the strike out totals in the FIP formula. In order to account for this slight change, we just need to go through and calculate a new constant to go along with the inclusion of PH%, and for the 2016 season it turns out to be 4.817. Read the rest of this entry »

Last year, when Statcast data initially became public, I set forth to find a more objective manner to classify and evaluate how well struck a given batted ball may be.  By the middle of last season, I settled upon a stat that I call Value Hits (VH), which is built on top of my xOBA stat.  I’ve previously described in detail how I evaluate batted balls for xOBA, but allow me to quickly summarize.  I am classifying each batted ball; finding what percent of balls with this classification go for one, two, three, or four bases; and multiplying each of those odds by the appropriate linear weight. I then sum these four values, and voilà I have the value of the batted ball. These values range from 0 (0% chance of a batted ball leading to bases) to about 2.02 (100% chance of a home run).

So, for example, say a ball is hit with a 102.5 mph exit velocity on a 21.5 degree vertical angle and 14.7 degree horizontal angle. That is a hard hit line drive hit right over the head of the second basemen, roughly. The following table shows how my system views a ball hit in this manner.

So, this line drive over the second baseman’s head turns out to have a value of 1.084.  It has a 76.9% chance of going into the gap for extra bases, and occasionally it will go over the fence for a home run. A very valuable hit indeed. It’s average value is close to that of a double, although the total value is dragged down some by the 20.7% chance of it being caught for an out.

I define a Value Hit as any batted ball with a total value equal to or greater than 0.88, which is, roughly, the value of a single.  This is a somewhat arbitrary cut off point, but it seems to work very well in identifying the truly well hit balls.  About 10.2% of the batted balls in 2016 are Value Hits, and 9.5% of the balls in play in 2015 were Value Hits. In xStats, I show Value Hits as a percent of total plate appearances, and about 7.1% of the plate appearances in 2016 have resulted in a Value Hit.

Stats for Value Hit Balls (VH):
8278 Hits in 9567 BIP.
3458 HR, 292 3B, 3044 2B, 63 SF.
.871/.871/2.344, 1.307 wOBA.

Note, these are actual stats, not predicted or expected stats. Yes, 3.189 OPS. Value Hits are exactly what it says on the tin: valuable. When you see me quoting VH%, know that I am talking about extraordinarily well struck baseballs, and the measurement and determination, while somewhat arbitrary in one sense, is also reasonably objective.

Read the rest of this entry »

The past few days I’ve been working on a tool that, I hope, will be capable of comparing the strengths of a pitcher to the strengths of a batter to build up pitcher vs batter match ups using the Statcast data and my xStats. This is a pretty big project for me, and it might take me a while to get it up and running. However, I have completed one small part of it: pitch values for each pitcher.

I’ve calculated various stats for each pitch type, for each pitcher in MLB. These include basic things like Swinging Strike, Called Strike, and Ball rates; the number of balls put in play; and average pitch speed. I also have average perceived velocity; average exit velocity; average launch angles; and xOBA, xBABIP, and xBACON. I’ve done this for each pitch type, as assigned by MLBAM. The MLBAM pitch designations are not perfect, and there is some pretty wide disparities between its classifications those given by other sources, such as Brooks Baseball, but these definitions are convenient so I will be using them for the moment.

Read the rest of this entry »

This year,  the reigning National League Champions have faced a large number of unexpected difficulties. Certainly the potentially career threatening injuries to Matt Harvey, David Wright, and to a lesser extent (in severity, not importance) have each put a damper on the season. Every team suffers injuries, under performance, and the occasional off the field or clubhouse problem. They’re all part of the game. One thing, though, the one thing that is truly remarkable, that is driving Mets fans up the wall at the moment, is their batting average with Runners in Scoring Position.

Or rather, Runners in Stranding Position, as many Mets fans now begrudgingly refer to it. This season, the Mets are competing for the lowest batting average with RISP in MLB history. They are currently tied with the 1963 Washington Senators with the third worst average since 1914. Only the 1968 Mets and 1969 Padres are worse. The 1968 Mets were on the upswing, getting ready to have their magical 1969 Miracle Mets season. The 1969 Padres, though, they were one of the worst teams in MLB history, finishing with a 48-114 record.

Before I start, I must acknowledge that there are divided factions around these sort of stats.  Many argue it is a statistical noise, arbitrary sampling, or selection bias. I’m inclined to agree, slicing up the season into such small chunks and looking at the numbers is bound to introducemany problems in a lot of different ways. However, there is another part of me that is deeply curious about this situation, and perhaps some of you out there might be as well. Yes, looking at splits in this manner is very dangerous, it probably has no predictive value at all. Then again, we are talking about a team putting up historic numbers in this brand new Statcast era, and our fancy new tools are just begging to be used. So lets have a looksie and see if there is anything to learn.

Read the rest of this entry »

We’re in the final days of July, and understandably much of the conversation has centered around the trade deadline. Trades are a great circus in many different ways, touching on a plethora of interests we fans have for the game. The deadline is the genesis of many of the greatest discussions in baseball, and it certainly deserves the mantle it has been given. However, the trade deadline, and the All Star Game for that matter, while fascinating, both occlude another interesting aspect of this game we love: July is the point of the season where both pitching and offense peak. Pitchers are, generally speaking, throwing at their highest velocities, with the greatest movement. Home runs are hit with the highest frequency and the longest distances. We are deep enough into the season that everyone is warmed up and locked in, but not quite deep enough for players to really begin suffering from fatigue. July is a great month for baseball.

It is important to keep in mind that the game goes through this natural peak towards the middle of the season, especially now that we have such a focus on increasingly abstract measures of the game coming from Statcast. We have exit velocities, angles, route efficiency, top speeds, on and on. This is all great information, you know that I love this stuff, but it is important to take a step back and recognize our own limitations when it comes to analyzing this information.

Read the rest of this entry »

In looking through the largest discrepancies between in game production and my various xStats, namely xOBA, I noticed that four of the largest differences come from the Yankees. Disturbingly, these are in the negative direction, meaning xStats are making a claim that these four Yankees are playing over their head and are due for a regression. Who are these Yankees? Didi Gregorius, Carlos Beltran, Jacoby Ellsbury, and Brett Gardner.

Three left handed hitters and a switch hitter playing in Yankee stadium, a ballpark geared for left handed production. On first blush, you might scoff and claim the stats aren’t accounting for the ballpark, these guys are probably benefiting from the short porch. Yes, they likely are benefiting to some degree from the short right field fence, but last season didn’t seem to have this problem with the xStats from these four batters.

Howie Kendrick has had an atypical season in 2016, defensively and offensively. He has played the vast majority of his career at second base, but this year he has filled the super utility role, with 253 innings in the outfield, 46 on first, 91 on third, and 139 on second. His manager, Dave Roberts, has had many positive things to say about Kendrick’s defense thus far, especially with his work filling in left field for the Dodgers.  He has shown range and an arm good enough to make a few nifty plays. Namely, throwing out Wilson Ramos at home. Ramos isn’t a speedster, and the throw was from very shallow left field, but the play did a lot to contribute to a win.  His offense, though, has been a bit of a let down. Especially for a guy who has been so consistent over the course of his career. Kendrick has never finished a season with a wOBA below .313, and only twice has his wOBA have been below average: 2006 (league average .332, Kendrick .313) and 2010 (league average .321, Kendrick .316). This year, to date, his wOBA has been .277, 42 points below average. Nonetheless, this isn’t the worst start to a season in his career.  At this point in 2009, he had a very similar slash line as he does today:

In the second half of 2009, he produced .356/.393/.544, finishing the season .291/.334/.444 with a .340 wOBA. Obviously, the Dodgers would be ecstatic to see that level of production in the second half of this season, but does he have it in him?

Read the rest of this entry »